《人工智能》教学大纲

课程代码

045101492

课程名称

人工智能

英文名称

Artificial Intelligence

课程类别

专业基础课

课程性质

必修

学时

总学时:40  实验学时:0  实习学时:0 其他学时:0

学分

2.5

开课学期

第四学期

开课单位

计算机科学与工程学院

适用专业

计算机科学与技术全英创新班、全英联合班

授课语言

英文授课

先修课程

算法设计、离散数学

课程对毕业要求的支撑

1.2能够应用计算机工程基础和专业知识解释模型的数理含义,对模型进行正确的推理,对专业工程问题进行专业分析;

2.2 能够基于数学、自然科学和工程科学的基本原理和数学模型,并借助文献研究分析复杂工程问题的特性;

2.3 能认识到解决复杂工程问题有多种方案可选择,能通过文献寻求可能的解决方案;

3.2能够运用多种知识提出解决计算机复杂工程问题的多种方案,对多种设计方案进行比较,提出的方案体现创新意识;

4.2能够针对计算机工程相关的各种控制规律、环节和系统,设计和实施实验方案;

5.2 能够选择与使用恰当的编程语言、数据资源、算法、软件工程等工具对计算机相关复杂工程问题进行分析、计算,设计和开发计算机系统。

课程目标

完成课程后,学生将具备以下能力:

本课程将探索计算机如何解决问题、从历史数据信息和玩游戏。当完成本课程,我们希望同学们能掌握人工智能的基础知识 并为他们进一步学习人工智能深入的知识和研究做准备。

课程简介

人工智能专注于智能计算机系统的发展,是计算机科学中最古老的学科之一。本课程将探索计算机如何解决问题、从历史数据信息和玩游戏。

教学内容与学时分配

  1. 人工智能导论(3学时)

    • 思政建设:智慧社会中的人工智能

    • 提供人工智能的基础知识,讨论人工智能的历史发展和什么是人工智能。

  2. 智能体       (3学时)

    • 学生们会学习如何设计简单的智能体以解决问题和PEAS描述方法来描述智能任务环境。

  3. 通过搜索解决问题   (6学时)

    • 学习深度优先等搜索方法并用于解决问题。

  4. 有信息的搜索   (6学时)

    • 通过使用启发函数来帮助搜索并设计适当启发函数。

  5. 限制满足问题          (6学时)

    • 学习如何解决限制满足问题

  6. 对抗性搜索(6学时)

    • 介绍不同的minimax方法并寻找游戏中的最优解。

  7. 通过观察的学习 (6学时)

    • 学习如何通过观察样本来学习和如何构建决策树来解决问题。

  8. 规划(4学时)

    • 学习如何通过规划来解决问题,包括全顺序和部分顺序规划等。


实验教学(包括上机学时、实验学时、实践学时)

教学方法

课堂教学、课外作业等为主。

考核方式

本课程注重过程考核,成绩比例为:

平时考评、测试:40%

期末考试(闭卷):60%

教材及参考书

现用教材:Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson, 2009


制定人及制定时间

吴永贤, 20191017


 “Artificial Intelligence” Syllabus

Course Code

045101492

Course Title

Artificial Intelligence

Course Category

Specialty Basic Courses

Course Nature

Compulsory Course

Class Hours

40 teaching sessions, including 0 lab sessions

Credits

2.5

Semester

The fourth semester

Institute

School of Computer Science and Engineering

Program Oriented

Computer Science and Technology Creative Full English Creative Class and Full English United Class

Teaching Language

English

Prerequisites

Discrete Mathematics, Algorithm Design and Analysis

Student Outcomes

 (Special Training Ability)

1.2能够应用计算机工程基础和专业知识解释模型的数理含义,对模型进行正确的推理,对专业工程问题进行专业分析;

2.2 能够基于数学、自然科学和工程科学的基本原理和数学模型,并借助文献研究分析复杂工程问题的特性;

2.3 能认识到解决复杂工程问题有多种方案可选择,能通过文献寻求可能的解决方案;

3.2能够运用多种知识提出解决计算机复杂工程问题的多种方案,对多种设计方案进行比较,提出的方案体现创新意识;

4.2能够针对计算机工程相关的各种控制规律、环节和系统,设计和实施实验方案;

5.2 能够选择与使用恰当的编程语言、数据资源、算法、软件工程等工具对计算机相关复杂工程问题进行分析、计算,设计和开发计算机系统。

Course Objectives

The objective of this course is to equip students with basic knowledge about AI. Students will learn how to solve a problem by searching solutions in a search space, problem solving under constraints and planning with multiple tasks. We will also provide introduction to decision trees and genetic algorithms. We hope this AI course could stimulate students' passion of learning and research in AI fields.

Course Description

Artificial intelligence (AI) is one of the fundamental topics in computer science. In the early days of computer science development, people had put a great expectation on computer to act and think like human. By this expectation, AI has been one of hottest research areas in computer science and has a great influence to our daily life. Students will explore what is AI and how AI works to help us in problem solving.

Teaching Content and Class Hours Distribution

  1. Introduction to AI(3 hours)

    • Provide basic knowledge of AI to students. Historical development of AI and what is AI will be discussed.

  2. Intelligent Agents(3 hours)

    • Students will learn how to design a simple intelligent agent to solve a given problem in this chapter. PEAS description about agent task environment will also be introduced.

  3. Solving Problem by Searching(6 hours)

    • Try to solve problem by uninformed search techniques, e.g. depth-first search.

  4. Informed Search(6 hours)

    • Solve problem by informed search. This chapter will introduce how to use heuristic function to aid searching. Students will also learn techniques for designing a heuristic function.

  5. Constraint Satisfaction Problems(6 hours)

    • Students will learn techniques for solving constraint satisfaction problems.

  6. Adversarial Search(6 hours)

    • This chapter introduces several variants of minimax method to find optimal decision in game with and without chance elements.

  7. Learning from Observation (6 hours)

    • By collecting samples (observations), we could learn patterns from them. Students will learn basic idea and algorithm of decision tree and other methods to learn from observation

  8. Planning(4 hours)

    • Students will learn to design of plan for problem solving. Planning with state-space search and partial order planning will be introduced in this chapter.


Experimental Teaching

No experiment

Teaching Method

Traditional teaching and homeworks

Examination Method

Quiz and tests: 40%

Final exam: 60%

Teaching Materials and Reference Books

Textbook:

Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson, 2009Reference


Prepared by Whom and When

Wing Yin NG, 17 October 2019.